TRACE

Trajectory Representation and Composition Estimator (TRACE) is a novel multi-task PFN-style Deep Learning model, fine-tuned for cell developmental coordinate prediction in early human cerebral organoid sc-RNA-seq data.

Joint-rotational project at Queen Mary University London, University College London and The Alan Turing Institute under Prof. Julien Gautrot, Prof. Yanlan Mao and Dr. Isabel Palacios and Dr. Federico Nanni.

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Table of Contents

• Background
• Repository Structure
• Installation
• Data Requirements
• Usage
• Known Issues & Limitations
• Contributing
• Citation
• License

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Background

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Repository Structure

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Installation

Prerequisites

• Python 3.12+

1. Clone the repository

git https://github.com/ChristianLangridge/TRACE.git
cd TRACE

2. Create and activate a conda environment using smt_pipeline.yml

conda env create -f TRACE.yml 

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Data Requirements

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Usage

1. Package registration

Before running scripts, register the package using pip install -e . from the project root with pyproject.toml.

2. Data implementation

Place all raw data within a 'data/raw/' folder so the path-finding system can retrieve it.

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Known Issues & Limitations

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Contributing

This is an active research project. If you'd like to contribute:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/your-feature)
3. Commit your changes (git commit -m 'Add: your feature')
4. Push and open a Pull Request
5. Add a dated, detailed comment/annotation in the CHANGELOG.md file of the change

Please ensure any new scripts avoid hardcoded paths and include basic inline documentation.

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Citation

If you use this codebase or the TRACE architecture in your work, please cite:

Langridge, C. (2025–2026). TRACE.
Joint-rotational project, Queen Mary University London, University College London
and The Alan Turing Institute.
https://github.com/ChristianLangridge/TRACE

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License

This project is licensed under the BSD-3-Clause License. See LICENSE for details.

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Developed as part of a joint rotational PhD project at Queen Mary University London, University College London and The Alan Turing Institute, under Prof. Julien Gautrot, Prof. Yanlan Mao, Dr. Isabel Palacios and Dr. Federico Nanni.